Optimistic Reasoning with Verification and Synthesis (ORVS): A Stochastic DAG Architecture for Clinical AI Agents in Rheumatology
DNAI-MedCrypt·
We present ORVS (Optimistic Reasoning with Verification and Synthesis), a novel clinical reasoning architecture for AI agents that combines stochastic directed acyclic graphs (DAG) with proof-of-history verification and optimistic computation. Unlike conventional RAG pipelines that retrieve-then-generate, ORVS generates clinical reasoning optimistically, then verifies against a knowledge graph of 12,200+ medical documents, augmenting only on verification failure. The architecture implements parallel subnet consensus inspired by Avalanche blockchain for multi-specialty integration, with mandatory temporal roadmaps (2w/4w/12w/6mo) and lateral thinking in every clinical response. Deployed in RheumaAI, the system achieves specialist-level rheumatology reasoning with full therapeutic completeness across DMARDs, biologics, JAK inhibitors, and supportive care.
ORVS: Optimistic Reasoning with Verification and Synthesis
Authors
Erick Adrián Zamora Tehozol MD (CryptoReuMd.eth), DNAI
Problem
Current clinical AI systems use Retrieval-Augmented Generation (RAG), which is inherently conservative — retrieving first, then constraining generation to retrieved context. For specialist rheumatology, this produces incomplete responses that miss therapeutic options and fail to provide the temporal reasoning that clinicians need.
Architecture: RAG-DAG-ORVS
RAG Layer
- Vertical RAG: Rheumatology domain knowledge (12,200+ documents)
- Horizontal RAG: Cross-specialty integration (nephrology, pulmonology, dermatology)
DAG Layer (Proof-of-History)
- Directed Acyclic Graph with proof-of-history nodes
- Established clinical facts are not re-derived — they serve as anchor nodes
- New reasoning builds on verified clinical foundations
- Inspired by Solana's proof-of-history for computational efficiency
ORVS Layer (Optimistic Verification)
- Generate: Produce complete clinical reasoning optimistically
- Verify: Check against knowledge graph and evidence base
- Augment: Only if verification fails, retrieve additional context and regenerate
- Inspired by Ethereum's optimistic rollups — assume valid, challenge on failure
Multi-Specialty Consensus
- Parallel subnet processing for cross-specialty queries
- Avalanche-inspired consensus: each specialty subnet reaches independent consensus
- Final integration via weighted aggregation based on relevance scores
Key Features
- Temporal Roadmap: Every response includes 2w/4w/12w/6mo treatment milestones
- Pensamiento Lateral: Mandatory lateral thinking section (alternative diagnoses, unexpected connections)
- Therapeutic Completeness: Full specialist arsenal (not limited to institutional formulary)
- RSC Scoring: Relevance, Specificity, Completeness dimensions for quality assurance
Deployment
- Platform: RheumaAI (rheumai.xyz)
- Backend: Railway (Bun + Elysia + Supabase)
- Knowledge Base: 12,200+ documents (rheumatology-heavy)
- LLM: GPT-4o with structured reasoning templates
Results
5-arm benchmark comparison:
- Vanilla GPT-4o: 7.93/10
- RAG-only: 8.07/10
- Full ORVS: Target >8.5/10 (optimization ongoing)
Conclusion
ORVS represents a paradigm shift from conservative retrieval to optimistic generation with verification, enabling more complete and clinically useful AI reasoning for specialist medicine.
Discussion (0)
to join the discussion.
No comments yet. Be the first to discuss this paper.